practice a little every day, paws crossed ♡
Machine Learning Engineer Interview Questions
Practice 9403 coding, system design and machine learning questions from 546 companies, sorted by role, topic and interview round. Every question has step-by-step hints, a code runner that supports 8 languages and an AI mentor; members also get the full solution walkthrough.
- 9403
- Questions
- 546
- Companies
- 11
- Topics
- 8
- Languages
Showing 91–120 of 381 results
- GoogleMediumMachine Learning EngineerExplain modeling challenges and fixes
This question evaluates end-to-end ML ownership and operational troubleshooting skills, including detection of data and modeling issues, trade-off analysis, and evidence-based impact measurement within the Machine Learning/MLOps domain.
Machine LearningTechnical Screen - GoogleMediumMachine Learning EngineerExplain ranking cold-start strategies
This question tests your ability to design strategies for surfacing new items and serving new users in a large-scale recommendation system while maintaining relevance and diversity.
Machine LearningTechnical ScreenRecommender SystemsCold StartEmbeddings - GoogleHardMachine Learning EngineerExplain transformer architecture and variants
This question evaluates a candidate's mastery of transformer architecture and related competencies such as attention mechanisms, positional encodings, encoder/decoder variants, computational complexity and scaling, stability and initialization practices, and adaptation of sequence models to molecular representations.
Machine LearningTechnical Screen - GoogleMediumMachine Learning EngineerImplement a Transformer Block with SwiGLU
This question evaluates implementation and reasoning skills for Transformer internals, testing competency in multi-head self-attention, SwiGLU gated feed-forward layers, residual connections, and layer normalization within the Machine Learning / deep learning domain.
Coding & AlgorithmsTechnical Screen - GoogleMediumMachine Learning EngineerImplement substring search and weighted sampling
This question tests the ability to implement a fast substring matching algorithm and a data structure that supports weighted random sampling from a dynamic collection of items.
Coding & AlgorithmsTechnical ScreenString MatchingKMPProbability - GoogleHardMachine Learning EngineerInterpret Language-Model Perplexity
Learn the exact likelihood basis of perplexity, correct evaluation details, tokenizer caveats, and limits as an LLM quality metric.
Machine LearningTechnical Screen - GoogleHardMachine Learning EngineerList regularization methods and trade-offs
This question evaluates competency in machine learning regularization and model generalization, covering familiarity with methods such as L1/L2, dropout, early stopping, data augmentation, label smoothing, mixup, normalization, Bayesian priors, and ensembling.
Machine LearningTechnical Screen - GoogleMediumMachine Learning EngineerMinimize Fence Painting Operations
This problem tests your ability to use a greedy approach or dynamic programming to find the minimum number of strokes required to paint a fence of varying heights.
Coding & AlgorithmsTechnical ScreenGreedyStackArrays - GoogleEasyMachine Learning EngineerPresent and Defend Your Research, Then Explain Your Research Interests
A research-focused interview question with no coding: introduce your own research, defend the details of a project under the interviewer's follow-up questions, and explain which research directions interest you. It tests problem framing, ownership of contributions, rigor in evaluation and fit with the team's work.
Behavioral & LeadershipTechnical Screen - HarveyMediumMachine Learning EngineerImplement a Spreadsheet With Formulas
This problem tests your ability to model cell dependencies using a directed graph, handle topological sorting for re-evaluation, and identify circular references.
Coding & AlgorithmsTechnical ScreenGraphsRecursionDFS - InceptionMediumMachine Learning EngineerImplement Autoregressive Decoding in PyTorch
This question evaluates a candidate's ability to implement autoregressive decoding and sampling strategies in PyTorch, testing competencies in sequence generation, probabilistic sampling methods (greedy, temperature, top-k, top-p), batching, and end-of-sequence handling.
Machine LearningTechnical Screen - IndeedMediumMachine Learning EngineerImplement MSE Loss and Linear Regression from Scratch
Implement the mean squared error loss and a linear regression model with fit and predict methods, written from scratch rather than with a library model. It tests deriving and coding the gradients, choosing a training method, feature scaling, input validation, and verifying the implementation.
Machine LearningTechnical Screen - InstacartHardMachine Learning EngineerExplain Core ML Concepts
This question tests the conceptual understanding of supervised learning foundations, the mathematical relationship between regression techniques, and the convergence behavior of optimization algorithms.
Machine LearningTechnical ScreenLinear RegressionLogistic RegressionOptimization - InstacartHardMachine Learning EngineerSolve Two Sorted-Array Tasks
This problem tests your ability to efficiently merge and find elements in sorted arrays, focusing on optimal time complexity and edge case handling.
Coding & AlgorithmsTechnical ScreenArraysBinary SearchTwo Pointers - IntuitMediumMachine Learning EngineerLongest Meeting-Free Business-Hours Block per Employee and Day
Find each employee’s longest meeting-free business-hours interval using clipped attendance, merged overlaps, complete day coverage, and SQL window logic.
Data Manipulation (SQL/Python)Technical Screen - J.P. MorganMediumMachine Learning EngineerDesign an Entitlement-Aware Agentic Portfolio Workflow
This problem tests the ability to architect an LLM-based agentic system that balances autonomous task execution with strict role-based access control (RBAC) and auditability.
System DesignTechnical ScreenAgentic WorkflowSecurityLLM - J.P. MorganMediumMachine Learning EngineerReturn Every Index Pair Whose Values Sum to a Target
This problem tests your ability to handle frequency counts and index tracking using hash maps to identify all possible index pairs that sum to a specific target, particularly in the presence of duplicate values.
Coding & AlgorithmsTechnical ScreenHash MapArraysAlgorithms - Jane StreetMediumMachine Learning EngineerBoard Simulation: Bottom-Insert Pieces with Uniform Row and Column Detection
This question evaluates the ability to simulate a 2-D game board and maintain dynamic state invariants, testing array manipulation, state-tracking, and detection of uniform rows and columns under bottom-insert semantics.
Coding & AlgorithmsTechnical Screen - Jane StreetHardMachine Learning EngineerBuild a time-series forecasting model
Build a time-series forecasting model evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningTechnical Screen - Jane StreetMediumMachine Learning EngineerCode Editor with Block Shrink and Expand (Code Folding)
This question evaluates a candidate's ability to model and manipulate indentation-based hierarchical structures, maintain nested visibility state for collapsed blocks, and map between original file lines and dynamic display numbering.
Coding & AlgorithmsTechnical Screen - Jane StreetMediumMachine Learning EngineerConnect-N on an Infinite Board with Gravity
This question evaluates implementation skills for efficient algorithms and data structures to manage a sparse, unbounded grid with gravity and to detect N-in-a-row runs both vertically and horizontally.
Coding & AlgorithmsTechnical Screen - Jane StreetHardMachine Learning EngineerDesign an end-to-end training framework
Design an end-to-end training framework evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignTechnical Screen - Jane StreetHardMachine Learning EngineerDesign and implement a tiny language runtime
Design and implement a tiny language runtime evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignTechnical Screen - Jane StreetHardMachine Learning EngineerImplement compiler for custom language
This interview question evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer for Implement compiler for custom language states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignTechnical Screen - Jane StreetEasyMachine Learning EngineerMarketplace Data Store: Order Matching and End-of-Day Reconciliation
This question evaluates understanding of stateful data structures, deterministic order-matching logic, and time-ordered reconciliation of transaction logs, focusing on correct handling of pricing, tie-breaking, and re-opened inventory.
Coding & AlgorithmsTechnical Screen - Jane StreetHardMachine Learning EngineerPredict future time-series values
This interview question evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer for Predict future time-series values states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignTechnical Screen - LinkedInEasyMachine Learning EngineerCompute point-to-segment minimum distance
This problem tests your ability to apply vector projections and geometric constraints to find the shortest distance from a point to a line segment in a 2D plane.
Coding & AlgorithmsTechnical ScreenGeometryVectorsMath - LinkedInMediumMachine Learning EngineerCompute total covered interval length
Compute total covered interval length evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - LinkedInHardMachine Learning EngineerDesign a Model That Predicts Whether a Member Clicks a Displayed Job
An ML system design question: predict whether a member will click a job displayed to them, using member features, job content and interaction history. It tests defining the supervision signal and training data, choosing a loss, preventing data leakage, building member and job embeddings, and laying out the training and serving components.
ML System DesignTechnical Screen - LinkedInMediumMachine Learning EngineerDesign a Personalized Feed and Compare Ranking Objectives
Design an end-to-end personalized professional feed system and compare pointwise, pairwise, and listwise learning-to-rank formulations across offline training, online serving, and business metrics.
ML System DesignTechnical Screenml-system-designrecommendation-systemslearning-to-rank
Google
Harvey
Indeed
Instacart
Intuit
J.P. Morgan
Jane Street
LinkedIn